Interactive graphics with plotly

install.packages("plotly")

First Interactive Plot

library(tidyverse)
library(plotly)
p <- ggplot(data = midwest) +
  geom_point(mapping = aes(x = popdensity, y = percollege))
ggplotly(p)

Customized Interactive Plot

p <- ggplot(midwest, 
       aes(x = popdensity, y = percollege, color = state)) +
  geom_point() + 
  scale_x_continuous("Population Density", 
                     breaks = seq(0, 80000, 20000)) + 
  scale_y_continuous("Percent College Graduates") + 
  scale_color_discrete("State") + 
  theme_bw()
ggplotly(p)

Your Turn

  1. Using the starwars data, create a static ggplot and use the ggplotly function to turn it interactive.

Lord of the Rings Data

lotr <- read_tsv('https://raw.githubusercontent.com/jennybc/lotr/master/lotr_clean.tsv')
Parsed with column specification:
cols(
  Film = col_character(),
  Chapter = col_character(),
  Character = col_character(),
  Race = col_character(),
  Words = col_double()
)
lotr

Create plotly by hand

plot_ly(lotr, x = ~Words) %>% add_histogram()

Subplots

one_plot <- function(d) {
  plot_ly(d, x = ~Words) %>%
    add_histogram() %>%
    add_annotations(
      ~unique(Film), x = 0.5, y = 1, 
      xref = "paper", yref = "paper", showarrow = FALSE
    )
}

lotr %>%
  split(.$Film) %>%
  lapply(one_plot) %>% 
  subplot(nrows = 1, shareX = TRUE, titleX = FALSE) %>%
  hide_legend()

Grouped bar plot

plot_ly(lotr, x = ~Race, color = ~Film) %>% add_histogram()

Plot of proportions

## number of diamonds by cut and clarity (n)
lotr_count <- count(lotr, Race, Film)
## number of diamonds by cut (nn)
lotr_prop <- left_join(lotr_count, count(lotr_count, Race, wt = n), 
                       by = 'Race')

lotr_prop %>%
  mutate(prop = n.x / n.y) %>%
  plot_ly(x = ~Race, y = ~prop, color = ~Film, width = 900) %>%
  add_bars() %>%
  layout(barmode = "stack")

Your Turn

  1. Using the gss_cat data, create a histrogram for the tvhours variable.
  2. Using the gss_cat data, create a bar chart showing the partyid variable by the marital status.

Scatterplots by Hand

plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
  add_markers()

Change symbol

plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
  add_markers(symbol = ~state)

Change color

plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
  add_markers(color = ~state, colors = viridis::viridis(5))

Line Graph

storms_yearly <- storms %>%
  group_by(year) %>%
  summarise(num = length(unique(name)))

plot_ly(storms_yearly, x = ~year, y = ~num) %>%
  add_lines()

Your Turn

  1. Using the gss_cat data, create a scatterplot showing the age and tvhours variables.
  2. Compute the average time spent watching tv by year and marital status. Then, plot the average time spent watching tv by year and marital status.

Highcharter; Highcharts for R

install.packages("highcharter")

hchart function

library(highcharter)
Registered S3 method overwritten by 'xts':
  method     from
  as.zoo.xts zoo 
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 
Highcharts (www.highcharts.com) is a Highsoft software product which is
not free for commercial and Governmental use
lotr_count <- lotr %>%
  count(Film, Race)

hchart(lotr_count, "column", hcaes(x = Race, y = n, group = Film))

A second hchart

hchart(midwest, "scatter", hcaes(x = popdensity, y = percollege, group = state))

Histogram

hchart(lotr$Words)

Your Turn

  1. Using the hchart function, create a bar chart or histogram with the gss_cat data.
  2. Using the hchart function, create a scatterplot with the gss_cat data.

Build Highcharts from scratch

hc <- highchart() %>%
  hc_xAxis(categories = lotr_count$Race) %>%
  hc_add_series(name = 'The Fellowship Of The Ring', 
                data = filter(lotr_count, Film == 'The Fellowship Of The Ring')$n) %>% 
  hc_add_series(name = 'The Two Towers', 
                data = filter(lotr_count, Film == 'The Two Towers')$n) %>%
  hc_add_series(name = 'The Return Of The King', 
                data = filter(lotr_count, Film == 'The Return Of The King')$n)
hc

Change Chart type

hc <- hc %>%
  hc_chart(type = 'column')
hc

Change Colors

hc <- hc %>%
  hc_colors(substr(viridis::viridis(3), 0, 7))
hc

Modify Axes

hc <- hc %>%
  hc_xAxis(title = list(text = "Race")) %>%
  hc_yAxis(title = list(text = "Number of Words Spoken"),
           showLastLabel = FALSE)
hc

Add title, subtitle, move legend

hc <- hc %>%
  hc_title(text = 'Number of Words Spoken in Lord of the Rings Films',
           align = 'left') %>%
  hc_subtitle(text = 'Broken down by <i>Film</i> and <b>Race</b>', 
              align = 'left') %>%
  hc_legend(align = 'right', verticalAlign = 'top', layout = 'vertical',
            x = 0, y = 80) %>%
  hc_exporting(enabled = TRUE)
hc

Your Turn

  1. Build up a plot from scratch, getting the figure close to publication quality using the gss_cat data.

Correlation Matrices

select(storms, wind, pressure, ts_diameter, hu_diameter) %>%
  cor(use = "pairwise.complete.obs") %>%
  hchart()

Leaflet Example

library(leaflet)

storms %>%
  filter(name %in% c('Ike', 'Katrina'), year > 2000) %>%
  leaflet() %>%
  addTiles() %>%
  addCircles(lng = ~long, lat = ~lat, popup = ~name, weight = 1,
             radius = ~wind*1000)

gganimate

install.packages("gganimate")
library(gganimate)

ggplot(storms, aes(x = pressure, y = wind, color = status)) + 
  geom_point(show.legend = FALSE) + 
  xlab("Pressure") + 
  ylab("Wind Speed (MPH)") +
  facet_wrap(~status) +
  theme_bw(base_size = 14) +
  labs(title = 'Year: {frame_time}') +
  transition_time(as.integer(year)) + 
  ease_aes('linear')
anim_save(filename = 'storms.gif')

Additional Resources

---
title: "Data Visualization - Interactive Graphics using R"
author: "Brandon LeBeau"
date: "June 12, 2019"
output: html_notebook
---

```{r setup_chunks, echo = FALSE}
knitr::opts_chunk$set(fig.width=12, fig.height=6, fig.cap = NULL) 
```

# Interactive graphics with plotly
```{r install, eval = FALSE}
install.packages("plotly")
```

# First Interactive Plot
```{r first_plotly, message = FALSE, fig.width = 9, fig.height = 6}
library(tidyverse)
library(plotly)
p <- ggplot(data = midwest) +
  geom_point(mapping = aes(x = popdensity, y = percollege))
ggplotly(p)
```

# Customized Interactive Plot
```{r custom_plotly, warning = FALSE, fig.width = 9, fig.height = 6}
p <- ggplot(midwest, 
       aes(x = popdensity, y = percollege, color = state)) +
  geom_point() + 
  scale_x_continuous("Population Density", 
                     breaks = seq(0, 80000, 20000)) + 
  scale_y_continuous("Percent College Graduates") + 
  scale_color_discrete("State") + 
  theme_bw()
ggplotly(p)
```

# Your Turn
1. Using the `starwars` data, create a static ggplot and use the `ggplotly` function to turn it interactive. 

# Lord of the Rings Data
- Data from Jenny Bryan: <https://github.com/jennybc/lotr>

```{r read_in_lotr, error = FALSE}
lotr <- read_tsv('https://raw.githubusercontent.com/jennybc/lotr/master/lotr_clean.tsv')
lotr
```

# Create plotly by hand
```{r plotly_by_hand, fig.width = 9, fig.height = 6}
plot_ly(lotr, x = ~Words) %>% add_histogram()
```

# Subplots
```{r subplots, fig.width = 9, fig.height = 6}
one_plot <- function(d) {
  plot_ly(d, x = ~Words) %>%
    add_histogram() %>%
    add_annotations(
      ~unique(Film), x = 0.5, y = 1, 
      xref = "paper", yref = "paper", showarrow = FALSE
    )
}

lotr %>%
  split(.$Film) %>%
  lapply(one_plot) %>% 
  subplot(nrows = 1, shareX = TRUE, titleX = FALSE) %>%
  hide_legend()
```


# Grouped bar plot
```{r plotly_group, fig.width = 9, fig.height = 6}
plot_ly(lotr, x = ~Race, color = ~Film) %>% add_histogram()
```

# Plot of proportions
```{r plotly_proportions, message = FALSE, fig.width = 9, fig.height = 6}
## number of diamonds by cut and clarity (n)
lotr_count <- count(lotr, Race, Film)
## number of diamonds by cut (nn)
lotr_prop <- left_join(lotr_count, count(lotr_count, Race, wt = n), 
                       by = 'Race')

lotr_prop %>%
  mutate(prop = n.x / n.y) %>%
  plot_ly(x = ~Race, y = ~prop, color = ~Film, width = 900) %>%
  add_bars() %>%
  layout(barmode = "stack")
```

# Your Turn
1. Using the `gss_cat` data, create a histrogram for the `tvhours` variable. 
2. Using the `gss_cat` data, create a bar chart showing the `partyid` variable by the `marital` status.

# Scatterplots by Hand
```{r plotly_scatter, fig.width = 9, fig.height = 6, warning = FALSE}
plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
  add_markers()
```

# Change symbol
```{r plotly_symbol, fig.width = 9, fig.height = 6}
plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
  add_markers(symbol = ~state)
```

# Change color
```{r plotly_color, fig.height = 6, fig.width = 9}
plot_ly(midwest, x = ~popdensity, y = ~percollege) %>%
  add_markers(color = ~state, colors = viridis::viridis(5))
```

# Line Graph
```{r plotly_line, fig.height = 6, fig.width = 9}
storms_yearly <- storms %>%
  group_by(year) %>%
  summarise(num = length(unique(name)))

plot_ly(storms_yearly, x = ~year, y = ~num) %>%
  add_lines()
```

# Your Turn
1. Using the `gss_cat` data, create a scatterplot showing the `age` and `tvhours` variables.
2. Compute the average time spent watching tv by year and marital status. Then, plot the average time spent watching tv by year and marital status.

# Highcharter; Highcharts for R
```{r install_highcharter, eval = FALSE}
install.packages("highcharter")
```

# `hchart` function
```{r hchart1, fig.height = 6, fig.width = 9}
library(highcharter)

lotr_count <- lotr %>%
  count(Film, Race)

hchart(lotr_count, "column", hcaes(x = Race, y = n, group = Film))
```

# A second `hchart`
```{r hchart2, fig.height = 6, fig.width = 9}
hchart(midwest, "scatter", hcaes(x = popdensity, y = percollege, group = state))
```

# Histogram
```{r hchart_hist, fig.height = 6, fig.width = 9}
hchart(lotr$Words)
```

# Your Turn
1. Using the `hchart` function, create a bar chart or histogram with the `gss_cat` data.
2. Using the `hchart` function, create a scatterplot with the `gss_cat` data.

# Build Highcharts from scratch
```{r hc_scratch, fig.width = 9, fig.height = 6}
hc <- highchart() %>%
  hc_xAxis(categories = lotr_count$Race) %>%
  hc_add_series(name = 'The Fellowship Of The Ring', 
                data = filter(lotr_count, Film == 'The Fellowship Of The Ring')$n) %>% 
  hc_add_series(name = 'The Two Towers', 
                data = filter(lotr_count, Film == 'The Two Towers')$n) %>%
  hc_add_series(name = 'The Return Of The King', 
                data = filter(lotr_count, Film == 'The Return Of The King')$n)
hc
```

# Change Chart type
```{r hc_chart, fig.width = 9, fig.height = 6}
hc <- hc %>%
  hc_chart(type = 'column')
hc
```

# Change Colors
```{r hc_change_colors, fig.width = 9, fig.height = 6}
hc <- hc %>%
  hc_colors(substr(viridis::viridis(3), 0, 7))
hc
```

# Modify Axes
```{r hc_axis, fig.height = 6, fig.width = 9}
hc <- hc %>%
  hc_xAxis(title = list(text = "Race")) %>%
  hc_yAxis(title = list(text = "Number of Words Spoken"),
           showLastLabel = FALSE)
hc
```

# Add title, subtitle, move legend
```{r hc_modify, fig.height = 6, fig.width = 9}
hc <- hc %>%
  hc_title(text = 'Number of Words Spoken in Lord of the Rings Films',
           align = 'left') %>%
  hc_subtitle(text = 'Broken down by <i>Film</i> and <b>Race</b>', 
              align = 'left') %>%
  hc_legend(align = 'right', verticalAlign = 'top', layout = 'vertical',
            x = 0, y = 80) %>%
  hc_exporting(enabled = TRUE)
hc
```


# Your Turn
1. Build up a plot from scratch, getting the figure close to publication quality using the `gss_cat` data.

# Correlation Matrices
```{r correlation}
select(storms, wind, pressure, ts_diameter, hu_diameter) %>%
  cor(use = "pairwise.complete.obs") %>%
  hchart()
```

# Leaflet Example
```{r leaflet}
library(leaflet)

storms %>%
  filter(name %in% c('Ike', 'Katrina'), year > 2000) %>%
  leaflet() %>%
  addTiles() %>%
  addCircles(lng = ~long, lat = ~lat, popup = ~name, weight = 1,
             radius = ~wind*1000)
```

# gganimate
```{r gganimate, eval = FALSE}
install.packages("gganimate")
```

```{r gganimate-example}
library(gganimate)

ggplot(storms, aes(x = pressure, y = wind, color = status)) + 
  geom_point(show.legend = FALSE) + 
  xlab("Pressure") + 
  ylab("Wind Speed (MPH)") +
  facet_wrap(~status) +
  theme_bw(base_size = 14) +
  labs(title = 'Year: {frame_time}') +
  transition_time(as.integer(year)) + 
  ease_aes('linear')
```

```{r save-animation}
anim_save(filename = 'storms.gif')
```

![](storms.gif)


# Additional Resources
* plotly for R book: <https://plotly-book.cpsievert.me/>
* plotly: <https://plot.ly/>
* highcharter: <http://jkunst.com/highcharter/index.html>
* highcharts: <https://www.highcharts.com/>
* htmlwidgets: <https://www.htmlwidgets.org/>
* gganimate: <https://gganimate.com/>
